Are pregnancy and parity associated with telomere length? A systematic review
Bibliographic record
Abstract
BACKGROUND: Women's reproduction requires increased energy demands, which consequently may lead to cellular damage and aging. Hence, Telomere Length (TL), a biomarker of biological aging and health status may possibly serve as a biomarker of reproductive effort. The aim of this systematic review is to evaluate telomere dynamics throughout pregnancy and the association between parity and TL. METHODS: A systematic search was conducted across seven databases including CINAHL, Cochrane, PsycINFO, Proquest, PubMed; Scopus; and Web of Science, using keywords and MeSH descriptors of parity and TL. Predefined inclusion and exclusion criteria were used to screen abstracts and titles. After the removal of duplicates, 3431 articles were included in the primary screening, narrowed to 194 articles included in the full-text screening. Consensus was reached for the 14 studies that were included in the final review, and the Newcastle-Ottawa scale (NOS) was utilized to assess the quality of the selected studies. A mini meta-analysis utilized JASP 0.17.3 software and included 4 applicable studies, comprising a total of 2564 participants to quantitatively assess the estimated effect size of parity on TL. RESULTS: Of the 11 studies reviewed on parity and TL, four demonstrated a negative correlation; one - a positive correlation and six -found no correlation. Studies demonstrating a negative correlation encompassed rigorous methodological practices possibly suggesting having more children is associated with enhanced telomere attrition. Of the four longitudinal studies assessing telomere dynamics throughout pregnancy, most found no change in TL from early pregnancy to postpartum suggesting pregnancy does not affect TL from early pregnancy to early postpartum. The meta-analysis revealed a negative, yet, non-significant effect, of the estimated effect size of parity on TL(ES = -0.009, p = 0.126, CI -0.021, 0.03). CONCLUSIONS: Studies assessing pregnancy, parity and TL yielded mixed results, most likely due to the different research methods utilized in each study. Improvements in study design to better understand the short-term effects of pregnancy on TL and the effect of parity on TL over time, include precise definitions of parity, comparisons of different age groups, inclusion of reproductive lifespan and statistically adjusting for potential confounders in the parity and TL relationship.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".